Example forward-difference: Difference between revisions
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The velocity data from a single beam for a single data segment can therefore be visualised as: | The velocity data from a single beam for a single data segment can therefore be visualised as: | ||
[[File:Velocity data.png|frameless|center| | [[File:Velocity data.png|frameless|center|400px]] | ||
The square of the velocity difference between bins separated by <math>\delta</math> bins is then evaluated for each <math>t</math>. So for <math>t_1</math>, bin 1 and <math>\delta=1</math>, we get: | The square of the velocity difference between bins separated by <math>\delta</math> bins is then evaluated for each <math>t</math>. So for <math>t_1</math>, bin 1 and <math>\delta=1</math>, we get: | ||
Revision as of 10:14, 14 November 2021
Consider the example of an ADCP with a beam angle of , configured with a vertical bin size of 10 cm, recording profiles at 1 second intervals with a data segment length of 300 seconds. The Level 1 QC of the data identified that good data was typically returned from bins 1 to 30.
The velocity data from a single beam for a single data segment can therefore be visualised as:

The square of the velocity difference between bins separated by bins is then evaluated for each . So for , bin 1 and , we get:
For bin 1 the squared velocity difference can be evaluated for , whilst for bin 2 it is restricted to , reducing by 1 with each bin, so that for bin 29, it can only be evaluated for and there are no options for bin 30. This is summarised as follows:

The mean is then taken across the 300 profiles in the data segment i.e.
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